Machine Learning Engineer

This role supports the development and modernization of the demand forecasting capabilities within the client’s digital fulfillment organization. The team is responsible for forecasting order volumes, units, and fulfillment capacity across multiple channels (OPU, Ship-to-Home, Drive Up) to optimize store operations planning. Working closely with data scientists and platform engineers, this role bridges ML research and production by scaling data processing workloads, building robust ML pipelines, and ensuring forecasting models run reliably at scale. The ideal candidate brings an ML engineering mindset-combining data engineering, pipeline orchestration, and software engineering skills-to modernize a complex forecasting ecosystem that directly impacts store labor planning and customer experience.

Machine Learning & Data Science Experience building and deploying ML models in production environments Hands-on experience with time series forecasting (Prophet, ARIMA, or similar) Understanding of hyperparameter tuning, model validation, and experiment tracking Familiarity with feature engineering and feature store concepts

Data Engineering & Scalability Proficiency converting pandas-based workloads to PySpark for large-scale processing Experience with distributed data processing frameworks (Spark, Dask, or Ray) Ability to optimize data pipelines for performance and cost efficiency Working knowledge of data formats (Parquet, CSV) and partitioning strategies Experience with BigQuery or similar analytical databases (table design, partitioning, clustering, writing/validating datasets)

ML Pipeline Orchestration Experience building ML pipelines using Kubeflow Pipelines (KFP), Vertex AI, or Airflow Understanding of pipeline component design, DAG orchestration, and caching strategies Ability to integrate data validation, model training, and deployment steps into workflows Experience with pipeline parameterization and configuration management

Software Engineering Strong Python proficiency with production-grade coding standards Ability to read, refactor, and extend existing codebases Version control experience (Git) and structured change management Familiarity with testing frameworks (pytest), dependency management (Poetry/UV), and code quality tools (pre-commit, linting)

Cloud & Infrastructure Hands-on experience with GCP (Vertex AI, Cloud Storage) or equivalent cloud platforms Familiarity with containerization (Docker) and container orchestration (Kubernetes) Experience with CI/CD pipelines for ML workflows Understanding of secrets management and environment configuration

Technical Skills: Nice to Have Experience with Ray for distributed ML training and inference Exposure to Hadoop ecosystem tools (Hive, HDFS, Spark on YARN) Knowledge of ML model monitoring and drift detection Experience with infrastructure-as-code (Terraform, Cloud Deployment Manager) Familiarity with retail, supply chain, or demand forecasting domains Experience working with data science teams to productionize research code Background in scaling ML systems from prototype to enterprise-grade deployments

TECHNICAL SKILLS Nice To Have Exposure to ML/analytics-driven systems or forecasting platforms Advanced performance tuning and scalability optimization experience Familiarity with retail, merchandising, or supply chain systems Experience supporting globally distributed teams across time zones Knowledge of automated alerting, runbooks, and operational playbooks